Data

Example structure of a “good” guide

Viral secondary structure

  • Manfredonia et al. NAR (2020)
    • SHAPE-MaP/DMS-MaPseq of in vitro refolded viral genome in ~500bp tiles
    • data: sequences of single-stranded (low Shannon entropy, high SHAPE) regions (3599 nt; 12.0% of genome)
    • per-guide metric: % of target labeled as single-stranded
  • Lan et al. bioRxiv (2020)
    • DMS-MaPseq in infected Vero E6 cells
    • data: coordinates of unstructured/structured regions (6010 nt; 20.1% of genome)
    • per-guide metric: % of target labeled as unstructured
  • Sun et al. Cell (2021)
    • icSHAPE in infected Huh7.5.1 cells & on extracted RNA
    • data: icSHAPE score [0,1] per nucleotide
    • per-the guide metric: mean icSHAPE score (higher = more likely to be single-stranded)
  • Huston et al. Molecular Cell (2021)
    • SHAPE-MaP of purified RNA from infected Vero E6 cells
    • data: RNAstructure connectivity table (CT) per nucleotide (12527 positions unbound; 41.9% of genome)
    • per-guide metric: % target labeled as unpaired
## Warning: Removed 35 rows containing non-finite values (stat_density).

## Warning: Removed 35 rows containing non-finite values (stat_density).

Correlation between annotations: over entire spacer target

  • Annotations from Manffedonia (2020) correlate poorly with other annotations
    • Only 12% of genome is labeled
    • Experimental data generated on 500bp tiling of refolded viral genome
  • Relatively high correlation between annotations generated from in vivo settings
    • Lan (2020)
    • Sun (2021)
    • Huston (2021)

Intersection of in vivo viral structure predictions

Correlation between annotations: over spacer seed region (positions 8-12 on spacer)

Exploratory data analysis: controls

Plate controls: 612_Control and No_Protein

  • Plate GS2-2 outliers: O 9-12, 21-24
  • No_Protein (+/- activator) and 612_Control (- activator) controls cluster tightly across plates
  • Variable spread of 612_Control activity with 100 fM activator

RNP-only controls

## Warning: Removed 1 rows containing non-finite values (stat_ydensity).

  • RNP-only control varies by plate (and presumably by guide)
  • Want to be able to account for additional gain in activator-dependent rate background
  • Outliers on plate GS2-1 all correspond to NCR_1344

Data analysis

Methods:

  • Mixed linear regression to compute rate per condition across triplicates:
    • per guide: \(\text{signal}_i(t) = (\beta_0 + \beta_{0,i}) + (\beta_{\text{100fM}}\cdot\mathbb{I}[\text{100fM}]) \ + (\beta_{t} + \beta_{t,i}) \cdot t \ + (\beta_{t:\text{100fM}}\cdot t\cdot\mathbb{I}[\text{100fM}])\)
    • RNP-only: \(\text{signal}_i(t) = (\beta_0 + \beta_{0,i}) + (\beta_t + \beta_{t,i}) \cdot t_i\)
    • 100fM activator: \(\text{signal}_i(t) = (\beta_0 + \beta_{0,i} + \beta_{\text{100fM}}) + (\beta_t + \beta_{t,i} + \beta_{t:\text{100fM}}) \cdot t_i\)
    • 100fM activator-dependent rate: \(\beta_{t:\text{100fM}}\)
  • Background subtract mean of empty wells
  • Exclude timepoints before 1000 seconds (first 10 timepoints show dip in some RNP-only controls)
  • Random effects (per 384-well, across timepoints): intercept, slope (wrt time)
  • Fixed effects: intercepts (\(\beta_0\), \(\beta_{\text{100fM}}\)), slopes (\(\beta_t\), \(\beta_{t:\text{100fM}}\))

Example traces:

  • dots: signal per 384-well per time
  • solid lines: regression fit

Results: 100 fM rates (above RNP-only control)

Summary of screen

  • Similar spread of rates across plates
  • Expect to see poorer rates for plates GS2-1 and GS2-2 (which harbor the “bad” guides)

Detection at last timepoint

Time to detection

Per guide: * Per timepoint: t-test for difference in signal between RNP-only and 100 fM conditions per timepoint * Perform FDR correction for number of measurements from start of experiment to timepoint * Return first timepoint for which corrected p-value < 0.05

Volcano plots of screening rates

  • Guides on plate GS2-2 have similar rates as to other plates, but are much more variable (p-values closer to 1)

Rates by position along SARS-CoV-2 genome

Structures of the two guides that performed well (rate > 1 above background) but without hairpin structure (NCR_1346, NCR_1351):

Rates across groups of guide secondary structure

  • Statistically significant (positive?) correlation between guide activity and spacer structure

Determination of how much of predicted hairpin structure needs to be maintained:

##         NCR.id                       spacer
## 120   NCR_1313         GUUUACCUUGGUAAUCAUCU
## 126   NCR_1319         UCAUUAAAUGGUAGGACAGG
## 137   NCR_1330         GCAAUCAAUGGGCAAGCUUU
## 138   NCR_1331         CUUCUCUGUAGCUAGUUGUA
## 139   NCR_1332         GAGUAAAUCUUCAUAAUUAG
## 142   NCR_1335         AUGGUGUCCAGCAAUACGAA
## 143   NCR_1336         GCCGUCUUUGUUAGCACCAU
## 155   NCR_1348         AUUAGCUCUCAGGUUGUCUA
## 156   NCR_1349         UGGUACGUUAAAAGUUGAUG
## 158   NCR_1351         UGGCUACUUUGAUACAAGGU
## 21685 NCR_1410 UGAAUGUAAAACUGAGGAUCUGAAAACU
## 9671  NCR_1412 UAUAAGCAAUUGUUAUCCAGAAAGGUAC
## 10691 NCR_1417 GAUUGAGAAACCACCUGUCUCCAUUUAU
##                                                          structure
## 120           ...((((((((.........))))................))))........
## 126           ...((((((((.........))))................))))........
## 137           .......(((((....(((...((........))..))).))))).......
## 138           ..(((.(((........(((((.........)))))......))).)))...
## 139           ...............(((((((..(((......)))...)))))))......
## 142           ................((..((((((((.....))))))))..)).......
## 143           ..............((((((((((........)))..)))))))........
## 155           (((((.((((......(((.((((............))))))))))))))))
## 156           ...((((((((.........))))........)))).((((((...))))))
## 158           (((.(((((((.........))))........))))))(((((...))))).
## 21685 ((((((.((((.........))))......((.....))........)).))))......
## 9671  ...(((.((((.........))))............(((....))).........)))..
## 10691 ...............(((((((((((......................)))))).)))))

  • “Good” guides exhibit similar 100fM rates as “bad” guides

Rates across guide ordering group

Background (RNP-only) rate by guide secondary structure

  • In guides that maintain crRNA hairpin: correlation between number of basepaired positions in spacer and background RNP-only rate

Other analyses

## Warning in cor.test.default(GC_content, Estimate, method = "spearman"): Cannot
## compute exact p-value with ties

## Warning in cor.test.default(GC_content, Estimate, method = "spearman"): Cannot
## compute exact p-value with ties
## Warning in is.na(x): is.na() applied to non-(list or vector) of type 'language'

## Warning in is.na(x): is.na() applied to non-(list or vector) of type 'language'

  • Weak negative correlation between spacer GC content and guide activity
## Warning in cor.test.default(downstream_U, Estimate, method = "spearman"): Cannot
## compute exact p-value with ties

## Warning in cor.test.default(downstream_U, Estimate, method = "spearman"): Cannot
## compute exact p-value with ties
## Warning in cor.test.default(downstream_unstructured_U, Estimate, method =
## "spearman"): Cannot compute exact p-value with ties

## Warning in cor.test.default(downstream_unstructured_U, Estimate, method =
## "spearman"): Cannot compute exact p-value with ties
## Warning in is.na(x): is.na() applied to non-(list or vector) of type 'language'

## Warning in is.na(x): is.na() applied to non-(list or vector) of type 'language'

## Warning in is.na(x): is.na() applied to non-(list or vector) of type 'language'

## Warning in is.na(x): is.na() applied to non-(list or vector) of type 'language'

  • Weak positive correlation between % unstructured U in 30 nt downstream of protospacer in viral genome and guide activity
## Warning in cor.test.default(gRNA_MFE, Estimate, method = "spearman"): Cannot
## compute exact p-value with ties

## Warning in cor.test.default(gRNA_MFE, Estimate, method = "spearman"): Cannot
## compute exact p-value with ties
## Warning in is.na(x): is.na() applied to non-(list or vector) of type 'language'

## Warning in is.na(x): is.na() applied to non-(list or vector) of type 'language'

Comparison of 20mers to 28mers

Guide activity by viral structure

  • Correlation between viral structure as annotated by Lan (2020)
    • But no correlation with other in vivo annotations (Sun and Huston)

k-means clustering of in vivo structures (Lan, Sun, Huston)

Restriction to spacer seed region (positions 8-12 on spacer)

Elastic net regression

Model 1: sequence + structure

  • sequence: spacer sequence +/- 5 nt
  • viral structure: spacer sequence +/- 5 nt

Model 2: only sequence

  • sequence: spacer sequence +/- 5 nt

Model 3: only structure

  • viral structure: spacer sequence +/- 5 nt

Model 4: only sequence (binary)

  • sequence: spacer sequence (A/T: 0 ; C/G: 1) +/- 5 nt

Model 5: sequence (binary) + structure

  • sequence: spacer sequence (A/T: 0 ; C/G: 1) +/- 5 nt
  • viral structure: spacer sequence +/- 5 nt

Model 6: rate ~ (antitag position 1) * (spacer structure) + (downstream unstructured U)

  • antitag position 1
  • spacer structure
  • downstream unstructured U

## 
## Call:
## glm(formula = Estimate ~ ., family = "gaussian", data = subset(model6_comparison_data_onehot, 
##     nchar(spacer) == 20, select = -spacer))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -35.872  -11.505   -1.238    8.439   59.810  
## 
## Coefficients: (1 not defined because of singularities)
##                           Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                27.8168     2.6737  10.404  < 2e-16 ***
## antitag_pos1_A              0.1987     3.1240   0.064    0.949    
## antitag_pos1_C              0.3098     3.5574   0.087    0.931    
## antitag_pos1_G            -19.4347     3.4947  -5.561 9.21e-08 ***
## antitag_pos1_U                  NA         NA      NA       NA    
## downstream_unstructured_U -16.6583    14.2724  -1.167    0.245    
## spacer_structure            6.9620     4.9248   1.414    0.159    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 280.6118)
## 
##     Null deviance: 65043  on 191  degrees of freedom
## Residual deviance: 52194  on 186  degrees of freedom
## AIC: 1635.1
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = Estimate ~ ., family = "gaussian", data = subset(model6_comparison_data_onehot, 
##     select = -spacer))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -37.871  -12.056   -0.602    8.752   56.402  
## 
## Coefficients: (1 not defined because of singularities)
##                             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                27.798637   2.643318  10.517  < 2e-16 ***
## antitag_pos1_A             -0.006136   3.049764  -0.002   0.9984    
## antitag_pos1_C              0.608373   3.463683   0.176   0.8608    
## antitag_pos1_G            -18.332086   3.419921  -5.360  2.3e-07 ***
## antitag_pos1_U                    NA         NA      NA       NA    
## downstream_unstructured_U -13.644894  13.729301  -0.994   0.3215    
## spacer_structure            9.614545   4.779510   2.012   0.0456 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 283.4019)
## 
##     Null deviance: 68755  on 203  degrees of freedom
## Residual deviance: 56114  on 198  degrees of freedom
## AIC: 1738.8
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = (Estimate > 20) ~ ., family = "binomial", data = subset(model6_comparison_data_onehot, 
##     nchar(spacer) == 20, select = -spacer))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.7203  -1.2990   0.7792   0.9761   2.1787  
## 
## Coefficients: (1 not defined because of singularities)
##                            Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                0.597273   0.337095   1.772   0.0764 .  
## antitag_pos1_A            -0.103502   0.387561  -0.267   0.7894    
## antitag_pos1_C            -0.007685   0.445336  -0.017   0.9862    
## antitag_pos1_G            -2.732521   0.599114  -4.561 5.09e-06 ***
## antitag_pos1_U                   NA         NA      NA       NA    
## downstream_unstructured_U -1.403859   1.813101  -0.774   0.4388    
## spacer_structure           0.789682   0.680800   1.160   0.2461    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 265.65  on 191  degrees of freedom
## Residual deviance: 226.28  on 186  degrees of freedom
## AIC: 238.28
## 
## Number of Fisher Scoring iterations: 4
## 
## Call:
## glm(formula = (Estimate > 20) ~ ., family = "binomial", data = subset(model6_comparison_data_onehot, 
##     select = -spacer))
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -1.830  -1.307   0.745   0.964   2.027  
## 
## Coefficients: (1 not defined because of singularities)
##                            Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                0.595755   0.332701   1.791   0.0733 .  
## antitag_pos1_A            -0.193418   0.379716  -0.509   0.6105    
## antitag_pos1_C             0.001186   0.439055   0.003   0.9978    
## antitag_pos1_G            -2.443452   0.524733  -4.657 3.22e-06 ***
## antitag_pos1_U                   NA         NA      NA       NA    
## downstream_unstructured_U -0.706516   1.733150  -0.408   0.6835    
## spacer_structure           1.087078   0.661398   1.644   0.1003    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 280.84  on 203  degrees of freedom
## Residual deviance: 243.49  on 198  degrees of freedom
## AIC: 255.49
## 
## Number of Fisher Scoring iterations: 4

Model 7: reduced features

  • antitag position 1
  • downstream unstructured U
  • spacer structure

Model 8: model cis cleavage site

  • anti-tag
  • target
  • cis cleavage site

Comparison to screening performed in gBlocks

## Warning in eval(substitute(expr), data, enclos = parent.frame()): NAs introduced
## by coercion
## Warning: NAs introduced by coercion
## [1] "mixed model failed: NCR_1320"
## [1] "mixed model failed: NCR_1332"
## [1] "mixed model failed: NCR_1387"
## Warning: Removed 27 rows containing non-finite values (stat_smooth).
## Warning: Removed 27 rows containing missing values (geom_point).
## Warning: Removed 5 rows containing missing values (geom_smooth).

gBlock round 2 outlier:

Figures for paper

Figure 1A (data): guide design pipeline

Figure 2A: range of observed guide activities

## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing non-finite values (stat_bin).
## Warning: Removed 2 rows containing missing values (geom_bar).

## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing non-finite values (stat_bin).
## Warning: Removed 2 rows containing missing values (geom_bar).

Figure 2B: example traces

Figure 2C: viral RNA v. gblock

Figure 3: elastic net regression + anti-tag result

## 
##  Welch Two Sample t-test
## 
## data:  subset(guide_rate$Estimate, guide_rate$antitag_pos1 != "G") and subset(guide_rate$Estimate, guide_rate$antitag_pos1 == "G")
## t = 7.3507, df = 67.711, p-value = 1.686e-10
## alternative hypothesis: true difference in means is greater than 0
## 95 percent confidence interval:
##  15.19083      Inf
## sample estimates:
## mean of x mean of y 
##  27.45234   7.80380
## 
##  Welch Two Sample t-test
## 
## data:  subset(guide_rate$Estimate, guide_rate$antitag_label == "G") and subset(guide_rate$Estimate, guide_rate$antitag_label %in% c("GU", "GUU"))
## t = 2.257, df = 30.276, p-value = 0.01569
## alternative hypothesis: true difference in means is greater than 0
## 95 percent confidence interval:
##  1.853323      Inf
## sample estimates:
## mean of x mean of y 
##  9.179261  1.712470

Figure 4C: LOD with Cas13-Csm6 tandem assay

Figure 4D: robustness to genetic variants

Suppl. Figure 1A: random forest variable importance

Suppl. Figure 1B: sequence logo

## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.

## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.

Suppl. Figure 2A: GC content

Suppl. Figure 2B: hybridization MFE

Suppl. Figure 2C: cleaveable U in target context

Suppl. Figure 3A: spacer structure

## Warning: Groups with fewer than two data points have been dropped.

## Warning: Groups with fewer than two data points have been dropped.

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Suppl. Figure 3B: structure of direct repeat

Suppl. Figure 4A: in vivo viral structure

Suppl. Figure 4B: genomic structure vs. rate

Suppl. Figure 5A: multiplex set of 40 vs. primary screen

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing missing values (geom_point).

Suppl. Figure 5B: leave-one-out counterscreen

Suppl. Figure 5C: human RNA counterscreen

## Warning in sort(as.numeric(guide)): NAs introduced by coercion

## Warning in sort(as.numeric(guide)): NAs introduced by coercion

## Warning in sort(as.numeric(guide)): NAs introduced by coercion

## Warning in sort(as.numeric(guide)): NAs introduced by coercion

## Warning in sort(as.numeric(guide)): NAs introduced by coercion

## Warning in sort(as.numeric(guide)): NAs introduced by coercion

## Warning in sort(as.numeric(guide)): NAs introduced by coercion

## Warning in sort(as.numeric(guide)): NAs introduced by coercion

Suppl. Figure 6: 32-pool vs. 8-pool w/ forced mismatch

## Warning: Ignoring unknown aesthetics: fill

gblock rates

## Warning: Removed 5 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing non-finite values (stat_bin).
## Warning: Removed 2 rows containing missing values (geom_bar).

anti-tag complementarity

interaction btwn anti-tag G and spacer structure

## Warning: Groups with fewer than two data points have been dropped.

## Warning: Groups with fewer than two data points have been dropped.